The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Combining social network and collaborative filtering for personalised manufacturing service recommendation
Owing to the rapid proliferation of Web service technologies in cross-enterprise manufacturing collaborations, information overload is becoming a major barrier that hinders the effective discovery of the shared manufacturing services provided by collaborative partners for supply chain deployment. Thus, we aimed to identify a different approach for discovering manufacturing services by making personalised service recommendations that are suited to the specific needs of active service users based on usage data from previous retrievals made by past service users. The proposed approach combines social network and collaborative filtering techniques in a unified framework to predict the missing Quality of Service (QoS) values of manufacturing services for an active service user, thereby improving the effectiveness of personalised QoS-aware service recommendations. The social network explores the usage of preference and tagging relationships among service users and manufacturing services in making personalised recommendation, which alleviates the data sparsity and the cold start problems that hinder the traditional collaborative filtering techniques. A case study and experimental evaluation demonstrate that the proposed approach can achieve the practicality and accuracy to personalised manufacturing service recommendations in a real application.
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